During the reporting period, AXOLOTL focused on implementing its core scientific and technical activities aimed at advancing research capacity in marine and maritime artificial intelligence. The work concentrated on the development, adaptation, and validation of advanced deep-learning methodologies for marine biodiversity assessment and maritime monitoring, using heterogeneous and large-scale datasets.
Key activities included the design of data pipelines supporting the ingestion, harmonisation, and preprocessing of multi-source datasets, such as Earth observation imagery and in situ marine data. Emphasis was placed on dataset quality, annotation strategies, and reproducibility, including the definition of common protocols and standards shared among partners. Expert-driven annotation campaigns supported supervised learning approaches, addressing challenges related to class imbalance and spatial variability.
On the modelling side, AXOLOTL implemented and tested state-of-the-art deep-learning architectures, including convolutional neural networks and segmentation models tailored to complex marine environments. Experimental work explored model generalisation across geographical areas and sensor types, as well as performance optimisation under realistic data constraints, enabling systematic evaluation and iterative refinement.
Joint scientific work was complemented by staff exchanges, technical study visits, and specialised training activities, facilitating direct knowledge transfer on advanced AI workflows, model evaluation techniques, and research software and data management practices.
In parallel, AXOLOTL supported scientific coordination through a shared research roadmap aligned with European research directions, including data-driven ocean observation and digital twin approaches. Preparatory work for joint scientific publications and future research activities was initiated, ensuring continuity beyond individual tasks.
Overall, the reporting period was characterised by the execution of core scientific activities, establishment of common technical frameworks, and consolidation of collaborative research practices underpinning the project’s AI-driven marine research agenda.
